Update app.py

app.py

@@ -1,107 +1,40 @@
 import streamlit as st
-import uuid
 import os
-import re
-from pydantic import BaseModel, Field
-
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.messages import BaseMessage
-from langchain_core.runnables.history import RunnableWithMessageHistory
-from langchain_core.runnables import ConfigurableFieldSpec
-from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate
 from langchain_groq import ChatGroq
+from dotenv import load_dotenv
 
-# --- Load environment variables from .env ---
-groq_api_key = os.environ["GROQ_API_KEY"]
-
-
-# --- In-memory session-based history ---
-class InMemoryHistory(BaseChatMessageHistory, BaseModel):
-    messages: list[BaseMessage] = Field(default_factory=list)
-
-    def add_messages(self, messages: list[BaseMessage]) -> None:
-        self.messages.extend(messages)
+load_dotenv()
 
-    def clear(self) -> None:
-        self.messages = []
-
-store = {}
-def get_by_session_id(session_id: str) -> BaseChatMessageHistory:
-    if session_id not in store:
-        store[session_id] = InMemoryHistory()
-    return store[session_id]
+groq_api_key = os.environ["GROQ_API_KEY"]
+groq_model = os.environ["MODEL_NAME"]
 
-# --- Prompt Template ---
 prompt = ChatPromptTemplate.from_messages([
-    ("system", "You are an intelligent AI bot which Loves chatting with people. You will answer them in a friendly tone. Try your best to solve the user's problem. Never talk about sensitive content"),
-    ("system", "If user asks about whats your purpose, you can say your a friendly bot happy to talk to the person"),
-    ("system", "If user asks about who made you: you were made by Merwin"),
-    MessagesPlaceholder(variable_name="history"),
+    ("system", "You are an intelligent AI bot that loves chatting with people. Answer in a friendly tone and do your best to help. Avoid sensitive content."),
+    ("system", "If user asks your purpose, say you're a friendly bot happy to talk."),
+    ("system", "If user asks who made you: you were made by Merwin Pinto, A talented student of Vishwakarma University."),
     ("human", "{question}")
 ])
 
 model = ChatGroq(
     groq_api_key=groq_api_key,
-    model_name="llama-3.1-8b-instant"
+    model_name=groq_model
 )
 
-chain = prompt | model
-
-# --- Chain with History Management ---
-chain_with_history = RunnableWithMessageHistory(
-    chain,
-    get_session_history=get_by_session_id,
-    input_messages_key="question",
-    history_messages_key="history",
-    history_factory_config=[
-        ConfigurableFieldSpec(
-            id="session_id",
-            annotation=str,
-            name="Session ID",
-            description="Conversation session ID.",
-            default="default-session",
-            is_shared=True,
-        )
-    ]
-)
-
-# --- Streamlit UI ---
 st.set_page_config(page_title="Mercy AI", layout="centered")
 st.title("🤖 Merwin's AI — Your AI Companion")
 st.subheader("Your journey matters. I'm here to listen, support, and guide you.")
 
-session_id = str(uuid.uuid4())  # You can also use a static ID if preferred.
-
-st.session_state['session_id'] = session_id  
-
-# --- Input Validation ---
-def is_valid_name(name):
-    return bool(re.fullmatch(r"[A-Za-z\s]{2,50}", name.strip()))
-
-def is_valid_age(age_str):
-    return age_str.isdigit() and 5 <= int(age_str) <= 100
-
-
-# --- Chat History Setup ---
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-
 st.markdown("Type your question below:")
 
 user_input = st.chat_input("Ask something...")
 
 if user_input:
     with st.spinner("Processing... Please wait."):
-        response = chain_with_history.invoke(
-            {
-                "question": user_input
-            },
-            config={"configurable": {"session_id": session_id}}
-        )
-        st.session_state.chat_history.append(("user", user_input))
-        st.session_state.chat_history.append(("bot", response.content))
-
-# --- Display chat messages ---
-for role, msg in st.session_state.chat_history:
-    with st.chat_message("user" if role == "user" else "assistant"):
-        st.markdown(msg)
+        chain = prompt | model
+        response = chain.invoke({"question": user_input})
+        
+        with st.chat_message("user"):
+            st.markdown(user_input)
+        with st.chat_message("assistant"):
+            st.markdown(response.content)